@inproceedings{aa0fab803c9b40a4858f4a89b5783d70,
title = "Toward usable evidence: Optimizing knowledge accumulation in HCI research on health behavior change",
abstract = "Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate - and use - knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.",
keywords = "Behavior change, Evaluation methods, Health informatics, User studies",
author = "Predrag Klasnja and Hekler, {Eric B.} and Korinek, {Elizabeth V.} and John Harlow and Mishra, {Sonali R.}",
note = "Funding Information: This work was supported by funding from the Robert Wood Johnson Foundation (PI: Hekler, 71995) and from the National Heart Lung, and Blood Institute (PI: Klasnja, 1R01HL125440). Publisher Copyright: {\textcopyright} 2017 ACM.; 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",
year = "2017",
month = may,
day = "2",
doi = "10.1145/3025453.3026013",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "3071--3082",
booktitle = "CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems",
}